The Moderating Effect of the Status of Using a Product on the Relationship between Brand Experience and Word of Mouth

(1)

Archives of Business Research – Vol. 9, No.2 Publication Date: February 25, 2021

DOI: 10.14738/abr.92.9677.

Ngatno, & Apriatni, E. P. (2021). The Moderating Effect of the Status of Using a Product on the Relationship between Brand Experience and Word of Mouth Recommendations. Archives of Business Research, 9(2). 41-59.

The Moderating Effect of the Status of Using a Product on the Relationship between Brand Experience and Word of Mouth

Recommendations

Ngatno

Departmen of Business Administration Diponegoro University

Apriatni, Endang P.

Departmen of Business Administration Diponegoro University

ABSTRACT

The purpose of this study was to examine the moderating effect of the status of using the product on the relationship between brand experience, satisfaction, loyalty and brand recommendation.

Structural equation modeling (SEM) with multi groups was used for data analysis. The results of this study indicate that there is a strong relationship between brand experience, satisfaction, loyalty and brand recommendation. The effect of brand experience and brand satisfaction on WOM recommendations was higher in the new user group than in the established user group. While the effect of brand loyalty on WOM recommendations was higher for established users than for new users. The effect of brand experience on brand satisfaction was greater for established users than for new users. While the effect of brand experience on brand loyalty was greater in the new-user group, it was not significant. Finally, the effect of brand satisfaction on brand loyalty was greater for established users than for new users.

Keywords: brand experience, brand satisfaction, brand loyalty, word of mouth, status of using a product.

INTRODUCTION

Word of mouth (WOM) recommendation is a source of information that can be used by consumers in determining product choices. The experience of consumers in using the brand of a product will be distributed to other consumers so that it affects attitudes in determining brand choices. Delivering a great customer experience is very important for any business. A positive customer experience provides exceptional benefits in the form of improved customer satisfaction, customer loyalty, and better word of mouth marketing (Klaus and Maklan 2013). WOM recommendations have an important role, especially for consumers because it is a very useful source of communication for consumers (Derbaix and Vanhamme 2003). In essence, WOM recommendations are a process of interpersonal communication between sender and receiver that can influence the attitudes and behavior of the recipient (Merton 1968). Zhang and Dellarocas (2006) asserted that WOM recommendations could ultimately replace traditional advertising. Other researchers suggest that

(2)

information from a personal referral source is stronger in influencing behavior, trust, and attitude towards the company.

Marketing research into WOM recommendations has tried to answer three questions 1) What are the consequences of WOM recommendations and their influence on consumer behaviour? 2) What are the antecedents that motivate consumers to engage in WOM recommendations? (Khan

& Rahman, 2015). 3) What strengthens or weakens the relationship between the antecedents and WOM recommendations? (Khan and Rahman 2015). Previous studies have focused on the consequences of communicators investigating "what happened to communicators after the WOM event?" Other studies have focused on the antecedents of communicators investigating the issue of "what makes people talk?" between variables stronger or weaker? In studying WOM recommendations can not only be studied as an independent variable, but must be studied comprehensively by investigating the antecedents, the mediator, and the moderator (Wien and Olsen 2012).

Some authors have claimed that a WOM recommendation is an antecedent of consumer behavior (Daugherty and Hoffman 2014), purchasing decisions (Voyer and Ranaweera 2015), brand equity (Virvilaite, Tumasonyte, and Sliburyte 2015), customer satisfaction (Shi et al. 2016), consumer judgment (Jeong and Koo 2015), and new product adoption (López and Sicilia 2013). However, research examining WOM recommendations as a result variable is still limited. A number of studies have shown that several antecedent variables influence WOM recommendations. Chung and Tsai (2009) showed that this binding force effect only applies to WOM providers who focus on prevention but not to WOM providers who focus on promotion. Sweeney, Soutar, and Mazzarol (2008) showed that the potential of WOM recommendations to affect perception or action depending on the relationship between sender and receiver, message content, delivery method and situation factors. Ismail and Spinelli (2012) revealed that direct brand love was a determinant of WOM recommendations, while the direct brand image was insignificant. Several variables can affect WOM recommendations, including product quality, customer satisfaction, customer loyalty, consumer trust, and perceived value (De Matos and Rossi, 2008). Jill Sweeney, Soutar, and Mazzarol (2014) showed that relative expertise, perceptual homo-philia, WOM recommendation strength, previous experience, and perceived brand equity had a positive impact on positive WOM recommendations. Although a number of studies that focussed on brand experience have shown the positive influence of several variables on WOM recommendations, there have been few studies of brand experience as an antecedent of WOM.

When studying consumer behaviour, the causal relationship between variables needs to consider moderating variables, because the strength of this relationship is likely to apply to one group but not to other groups. In recent years, the use of moderating variables has increased in the management literature, including the marketing literature, especially in predicting consumer behaviour. Some variables that can moderate the relationship include individual characteristics that cannot be controlled such as age, income, gender, and education (De Matos and Rossi, 2008), and situational characteristics such as perceived risk, shopping experience and length of relationship, continuance commitment, and brand-connection (Ranaweera & Menon, 2013;

Sicilia, Delgado-Ballester, and Palazon, 2016).

(3)

Archives of Business Research (ABR) Vol 9, Issue 2, February-2021

In this study, we propose the status of using a product as a moderating variable which is expected to influence the strength and weakness of the relationship between brand experience and WOM recommendations. Thus, this research developed a more comprehensive WOM model by integrating brand experience as an antecedent of WOM recommendations, satisfaction and loyalty as mediators, and the status of using a product as a moderator. Therefore, this study seeks to examine the moderating effect of the status of using a product on the relationship between brand experience and WOM recommendations. This research contributes to the literature on WOM recommendations and the results of this study could also be useful for marketing managers in developing marketing strategies, especially in efforts to improve marketing performance.

LITERATURE REVIEW, HYPOTHESIS, AND CONCEPTUAL FRAMEWORK

The Relationship between Brand Experience, Brand Satisfaction, Brand Loyalty, and WOM Recommendations

WOM is defined as informal, person-to-person communication between communicators and recipients that are considered non-commercial regarding products, services, brands, and organizations (Harrison-Walker, 2001). Brakus, Schmitt, and Zarantonello (2009) defined brand experiences as sensations, feelings, cognition, and behavioural responses to brand-related stimuli. According to Oliver (1997), satisfaction is defined as the pleasure of fulfilment. This means that consumers feel happy because their wants, needs and goals in using a product can be fulfilled. Consumption thus produces feelings of pleasure rather than displeasure.

Loyalty is a psychological process consisting of behavioural and attitude components. Hellier et al. (2003) defined loyalty as "the extent to which customers have demonstrated, to repeat the buying behaviour of a particular company's product or service". Brand loyalty is the decision of consumers to buy certain brands. Thus, brand loyalty is the power of desire to prefer one brand rather than one of the other available brands.

Worldwide, more than two-thirds of marketers surveyed agreed that brand experience is an effective way to achieve their organizational goals. Consumers are faced with the attributes and stimuli associated with the brand when they are going to buy a product (Babin, Hardesty, and Suter 2003). Customer responses to brand-related stimuli can be subjective internal responses (Brakus, Schmitt, and Zarantonello 2009) such as brand sensations, feelings, and cognition. Thus the more a product or service is used, the more consumer experience of the brand is used as an indication of happiness or satisfaction with the brand. This relationship is supported by several studies that show that brand experience as a construct affects the level of consumer satisfaction (Chen et al., 2014; Khan and Rahman, 2015; Klaus and Maklan, 2013). Therefore, in this study, we proposed the hypothesis:

H1: There is a positive effect of brand experience on satisfaction.

Brand loyalty has been proposed as the result of brand experience (Brakus et al., 2009;

Ramaseshan & Stein, 2014). In general, marketers think that brand experience is very good for building loyalty. When companies treat consumers well and give them experiences that they cannot find with other brands, loyalty will grow. When customers use a brand that provides a pleasant and memorable experience, the brand will be remembered by the consumers and result in a desire to repeat the purchase (Oliver 1997). Thus, a good brand can increase brand loyalty (Schmitt 2003). This is supported by several recent studies that show that brand experience

(4)

influences loyalty (Khan & Rahman, 2015; Moreira, Freitas, & Ferreira, 2017). Therefore, in this study, we proposed the hypothesis:

H2: There is a positive effect of brand experience on brand loyalty.

The behaviour of individuals can spread a positive message for a brand by WOM. Brand experience not only creates brand loyalty but also references the brand to other people (Morrison and Crane 2007). Consumers who already have experience with a brand will communicate with their relatives and friends in formal and informal groups (Westbrook, 1987;

Westbrook, 1987). Consumers in this group discussion and comment on their respective experiences (Fitzgerald Bone 1992). Besides, brands provide unique experiences to customers functionally and emotionally than those offered by companies (Berry, Seiders, and Grewal 2002).

A good brand can provide a superior experience to customers and so create greater affective ties.

Strong brands can be built by enhancing the positive experiences provided to customers (Alexandrov, Lilly, and Babakus 2013). Customers who have unique and memorable experiences of a brand recommend the brand to others, such as relatives and friends (Oliver 1997). Several previous studies have found that brand experience influences WOM recommendations (Barnes, Mattsson, & Sørensen, 2014; Beckman, Kumar, & Kim, 2013; Chen et al., 2014; Klaus & Maklan, 2013). Because brand experience is expected to have a positive impact on WOM recommendations, in this study, we proposed the hypothesis:

H3: There is a positive effect of brand experience on WOM recommendations.

In addition to being an outcome of brand experience, brand satisfaction can be an antecedent of brand loyalty and WOM recommendations. Brand satisfaction, as one of the antecedents of brand loyalty, has been widely recognized in various industries such as the retail industry (Tsai, Tsai, and Chang 2010), in cosmetic products, hospitality (Clemes, Gan, and Ren 2011) banking (Mohsan et al. 2011), tourism (Lai 2014) and communication (Edward and Sahadev 2011). By satisfying customers, companies can increase loyalty because customers intend or desire to buy the brand again. Thus, if the company's products are better than competing products in meeting customer needs, consumers will be loyal to the company's products. This is supported by research conducted in the service industry market, which shows that there is a positive relationship between customer satisfaction and loyalty (Klaus & Maklan, 2013; Moreira et al., 2017; Nysveen & Pedersen, 2014). Therefore, in this study, we proposed the hypothesis:

H4: There is a positive effect of brand satisfaction on brand loyalty.

Anderson & Sullivan (1993) state that brand satisfaction not only results in repeat purchases but also has an impact on WOM recommendations, which further strengthens brand loyalty. Fornell (1992) suggest that consumer satisfaction with a brand has a positive impact on WOM recommendations. When a customer is satisfied with their brand, they tend to be loyal to the brand. Several studies have shown that customer satisfaction has a positive effect on WOM recommendations (Klaus & Maklan, 2013). Therefore WOM recommendations are the key to determining future consumer purchasing decisions. With overall brand satisfaction, customer purchasing decisions are expected to be based on experience with brands that have been used.

Therefore, in this study, we proposed the hypothesis:

H5: There is a positive effect of brand satisfaction on WOM recommendations.

(5)

Loyalty to a brand is not only an outcome of brand experience and brand satisfaction, but it can also be a predictor of WOM communication of the brand. Loyal customers for a brand will try to invite new potential customers through positive stories of the products they have used. Several studies of loyalty have shown that this loyalty leads to positive behavioural intentions and customer behaviour (Bitner, Booms, & Tetreault, 1990). There are several consequences of loyalty to a brand, such as repeated purchases and brand recommendations to others (for example, by WOM). If someone has a psychological attachment to a brand (brand loyalty), then that person will be willing to share their experiences with others through WOM. Customer loyalty will have a positive effect on WOM recommendations (Gremler & Brown, 1999). Kazemi, PaEmami, Abbaszadeh, & Pourzamani (2013) have studied the impact of consumer loyalty on WOM recommendations and found that customer loyalty had a positive impact on WOM recommendations. In other words, loyal consumers tend to offer WOM recommendations to others. Therefore, brand loyalty has a positive effect on WOM recommendations in a brand context. Therefore, in this study, we proposed the hypothesis:

H6: There is a positive effect of brand loyalty on WOM recommendation.

Moderating Effect of Product Usage Status

The effect of moderation variables was first used in the field of psychology, but it is now widely used in other research fields such as management. Moderation variables modify the traditional relationships between the independent and dependent variables (Sekaran and Bougie 2016).

Arnold (1992) define a moderator as a variable that influences the shape (slope) or strength of the relationship between the independent variable and a dependent variable. Bryman and Cramer (1999) state that moderating relationships occur when relationships are found for some sample categories but not for others. Everything associated with brands changes with changes in how individuals process information, perceive brands and commit to brands (Ranaweera and Menon 2013). Therefore, there is a need to reexamine the relationships between brand experience, satisfaction, loyalty, and traditional WOM recommendations by exploring moderation variables that could influence the relationship between independent and dependent variables.

This moderated assessment of the relationship is important because it allows the researcher to avoid biased conclusions from the entire sample, which actually only applies to a portion of the sample (Baron and Kenny 1986).

In this study, the status of using a product variable (one of the individual characteristics that can be controlled) was used as a variable that was expected to moderate the relationship between brand experience, satisfaction, loyalty, and WOM recommendations. We classified the moderator variables into two groups: the new user group and the established user group. We assumed that the established user group had more information or knowledge, had more or longer contact with the product, and had a lower risk perception.

Walsh et al., (2008) referred to information processing theory and stated that customers with greater knowledge or expertise consider information cues for product evaluation. Existing studies of buyer behaviour confirmed that the level of knowledge consumers have about a product tends to influence product brand evaluation and attitude strength (Rao & Monroe, 1988).

Consumers who have this knowledge will be more confident in evaluating overall product quality based on technical attributes such as quality, benefits, and return. Furthermore, Walsh et al., (2008) stated that knowledgeable consumers would be less emotional and more objective in

(6)

assessing a product. The main implication of increasing knowledge is a behaviour change.

Therefore, consumers who have a lot of knowledge have a lot of information to convey to others.

This is supported by the opinion of Tuu, Olsen, & Linh (2011), which was that objective knowledge positively moderates the relationship between satisfaction and loyalty. In the context of long-standing relationships, consumers who have long used the product have more and longer involvement with that brand.

Finally, consumers who have long used a product have a lower risk perception because they have more information, knowledge and a greater frequency of involvement with the brand. This risk can be in the form of financial risks, benefits, and social risks. Based on schema theory, individuals will use previous experiences to evaluate new information. A good experience from a customer encourages consumers to make a repurchase (Bianchi, Drennan, & Proud, 2014; Mishra, Dash, & Cyr, 2014). A strong brand image of a product can serve to reduce customer risk perceptions and increase customer intention to repurchase (Aghekyan, Forsythe, Kwon, &

Chattaraman, 2012; Bougoure, Russell-Bennett, Fazal-E-Hasan, & Mortimer, 2016).

We propose that product usage status moderates the relationship between brand experience, satisfaction, loyalty and WOM recommendations. This proposal is based on the honeymoon- hazard effect, where new customers will give a bigger WOM recommendation compared to old customers (Blut et al. 2011). This honeymoon-hazard effect is likely to strengthen the relationship between experience, satisfaction, loyalty and WOM recommendation. Thus, with an increase in brand experience, satisfaction, and loyalty, WOM recommendations increase for all customers but, because of the honeymoon-hazard-effect, newer customers will produce greater WOM recommendations than more established customers. We expected that at a higher age relationship, the rate of WOM recommendations would decrease. Therefore, we expected the effect of brand experience, satisfaction and loyalty on WOM recommendations to be higher for new users than for established users. Therefore, in this study, we proposed the hypotheses:

H7: For established users, the influence of brand experience on (a) satisfaction and (b) loyalty will be higher, but the influence on (c) WOM recommendations will be lower.

H8: For established users, the influence of brand satisfaction on (a) loyalty will be higher and on (b) WOM recommendations will be lower.

H9: For established users, the influence of loyalty on WOM will be lower.

(7)

Fig.1. Research Framework METHOD

Sample and Data Collection

The Wacoal brand was chosen as the focus of this study. This brand is a well-known brand of women's lingerie and underwear (hedonic products). Since the product is women's underwear, promotion through WOM recommendations is more important than promotion through the mass media. Advertising women's clothing products through the mass media (TV, Billboards, Magazines, and newspapers) is prohibited by several countries, including Indonesia, because of concerns that it will lead to pornography. The target population of this research was customers of the Wacoal brand in Indonesia. The scope of the sample was limited to individuals over the age of 18 who have purchased the Wacoal brand in the past year.

In this study, data was obtained by distributing questionnaires to Wacoal customers for them to complete. Of the 200 questionnaires distributed, 192 were completed in full and eight were incomplete. The collected data were then classified into age groups (a) 56 years and over (N = 4;

2.1%) (b) 46–55 years (N = 7; 3.6%) (c) 36–45 years (N = 31; 16.1% ), (d) 26–35 years (N = 91;

47.4%) and (e) 18–25 years (N = 59; 30.7%). The status of using the Wacoal brand was classified as (i) new user (N = 96; 50.0%) and (ii) established user (N = 96; 50.0%). A profile of the demographic characteristics of the respondents is shown in Table 1.

(8)

Table 1. General characteristics of the sample

Variables frequency %

Age groups 18–25 years 59 30.7

26–35 years 91 47.4

36–45 years 31 16.1

46–55 years 7 3.6

56 years and above 4 2.1

Education High School 44 22.9

Undergraduate 74 38.5

Graduate 74 38.5

Occupation Civil servant 17 8.9

Private employee 123 64.1

Housewife 6 3.1

Student 46 23.9

Status of use New 96 50.0

Established 96 50.0

Measurement

The measurement of variables was developed from previous studies. The brand experience was adapted from Brakus et al. (2009). Brand satisfaction was also adapted from previous studies (Fullerton, 2005; Yi & La, 2004). Brand loyalty was measured by six items adapted from previous studies (Algesheimer, Dholakia, & Herrmann, 2005; Fullerton, 2005). Furthermore, to measure WOM recommendations, four items were adopted forom Carroll & Ahuvia (2006). Each item has five alternative answers with a Likert scale. To assess this measurement model, we used standardized loading factors, discriminant validity, and construct reliability. Standardized loading factors were used to measure convergent validity with values required to be > 0.60 (Hair et al.

2014). Furthermore, the discriminant validity is determined based on the average variance extracted (AVE) value. (Fornell & Larker, 1981). Meanwhile, to assess the reliability of construction is based on the reliability value of the composite, with values required to be > 0.60 (Bagozzi and Yi 1988).

(9)

Table 2. Measurement items

Construct Item Sources

Brand

experience Exp_1 "Wacoal" brand makes a strong

impression on my visual senses. Brakus, Schmitt, and Zarantonello (2009) Exp_2 I found this "Wacoal" brand

interesting in a sensory way

Exp_3 "Wacoal" brand induces my feelings.

Exp_4 I have strong emotions for this

"Wacoal" brand.

Exp_5 "Wacoal" brand is an emotional brand.

Exp_6 "Wacoal" brand produces its own experience for my body.

Brand

satisfaction Satisf_1 I am very satisfied with the service

provided by “Wacoal.” Grace and O’Cass 2005;

Fullerton 2005; Heitmann et al., 2007, Yi and Suna, 2004; Lyer, Rajesh and Muncy, James A. 2005

Satisf_2 I am very satisfied with “Wacoal.”

Satisf_3 “Wacoal” is well able to meet my needs.

Satisf_4 "Wacoal" provides very satisfying services.

Satisf_5 I believe that using “Wacoal” is usually a very satisfying experience.

Brand loyalty Loy_1 "Wacoal" brand will be my first

choice. Algesheimer, Uptal dan Herrmann, 2005;

dan Fullerton, 2005 Loy_2 I don't buy brands other than

"Wacoal" even though the same product is available.

Loy_3 I get more value for my money from

"Wacoal."

Word of

Mouth Wom_1 I recommend "Wacoal" to my

friends. Carroll & Ahuvia 2006

Wom_2 I recommend "Wacoal" to many people.

Wom_3 I encourage friends and family to buy "Wacoal."

Wom_4 I speak positively about "Wacoal."

Wom_5 I recommend the "Wacoal" brand to family.

Wom_6 I say positive things about "Wacoal"

to others.

Data Analysis

Data analysis was performed with AMOS 23. First, we assessed the overall model by looking at the results of the chi-square, goodnes of-fit index (GFI), adjusted goodness-of-fit (AGFI) and root mean square of the error approach (RMSEA). Furthermore, the structural model is assessed based on the strength of the relationship between constructs represented by each standard path coefficient. Following Cohen's (1988) recommendation, standardized path coefficients with an

(10)

absolute value < 0.10 indicated a small effect, a value of ~ 0.30 indicated a moderate effect and coefficients with an absolute value > 0.50 indicated a large effect. To test the moderation effect, a multi-group analysis was carried out, namely by determining the value of the Critical Ratio for Differences between Parameters to be greater than required (1.96). The structural estimates of the paths between the two groups were compared and then the values of the critical ratios for differences between parameters were used as the basis for assessing the significance of the moderating effect.

RESULTS Measurement Model

Confirmatory factor analysis (CFA) was carried out with SEM to determine whether a variable was valid or not. CFA was used to assess the suitability of the model before testing the structural model. The convergent validities of all constructs are summarized in Table 3. The constructive loading factor of brand experience was 0.811–0.842, brand satisfaction was 0.796–0.818, brand loyalty was 0.813–0.842 and WOM recommendations was 0.840–0.878. All factor loadings were above 0.60, which indicated convergent validity (Hair et al. 2014). The AVE values of all constructs were above 0.50 (0.643–0.678), which showed good convergent validity. The Construct Reliability (CR) was > 0.70 for all constructs. Internal consistency and the reliability of each factor can be determined from the construct reliability value and the AVE value. Thus, it was concluded that all variables studied were valid and reliable.

Table 3. CFA and Composite Reliability

Constructs Items Mean SD Factor loadings Construct Reliability AVE

Brand experience

exp_1 3.76 0.953 0.824

0.832 0.678

exp_2 3.67 0.910 0.842 exp_3 3.68 0.921 0.807 exp_4 3.72 0.911 0.811 exp_5 3.72 0.900 0.822 exp_6 3.74 0.945 0.834

Brand satisfaction

satisf_1 3.77 0.934 0.810

0.848 0.643

satisf_2 3.73 0.850 0.785 satisf_3 3.74 0.905 0.801 satisf_4 3.83 0.925 0.818 satisf_5 3.83 0.846 0.796 Brand loyalty loy_1 3.80 0.967 0.842

0.805 0.677

loy_2 3.72 0.939 0.813 loy_3 3.81 0.886 0.813

Word of mouth

Wom_1 3.68 1.033 0.850

0.776 0.744

Wom_2 3.51 1.153 0.878 Wom_3 3.56 1.091 0.882 Wom_4 3.69 1.047 0.861 Wom_5 3.74 0.973 0.840 Wom_6 3.86 0.905 0.864

(11)

Table 4 summarizes the discriminant validity of all constructs. Correlations between different constructs were smaller than the square root AVE of the four constructs (diagonal-diagonal elements). This ensured that construct discriminant validity was fulfilled (Fornell and Larker 1981). The assumption of linearity and bias in the general method was not violated.

Table 4. Discriminant validity results

Constructs Brand experience Brand satisfaction Brand Loyalty WOM Brand experience 0.823*

Brand satisfaction 0.620 0.802*

Brand Loyalty 0.662 0.651 0.822*

WOM 0.722 0.701 0.753 0.863*

Table 5 shows the model compatibility indices. The values of GFI, CFI, and TLI (all > 0.9) and the RMSEA value (< 0.08) indicated good compatibility (Bentler, 1990).

Table 5. Model fit index

Fit indices Result Cut-off value Evaluation Chi Square 183.320 < 215.563 good

Probability 0.144 > 0.05 good

GFI 0.916 > 0.90 good

CFI 0.995 > 0.90 good

TLI 0.994 > 0.90 good

AGFI 0.892 > 0.90 marginal RMSEA 0.025 < 0.08 good Structural Model

The proposed model without the moderator (the status of using a product) was tested further. Figure 2 shows the path coefficients of the proposed model. These results indicated the direct impact of the predictor variables on the predicted effect variables. The results of the significance test for the direct effects are shown in Table 6. In can be seen from Table VI that no main hypothesis was rejected. That is, the effects of brand experience, brand satisfaction, and loyalty on WOM recommendations were positive and significant.

(12)

Fig.2. The effect of brand experience on WOM.

Table 6

Summary results of the structural model

Proposed hypothesis relationship Estimate SE CR p Rejected/supported Brand experience à Brand satisfaction 1.003 0.067 14.940 *** H1 (supported) Brand experience à Brand Loyalty 0.824 0.118 7.009 *** H2 (supported)

Brand experience à WOM 0.321 0.159 2.016 0.044 H3 (supported)

Brand satisfaction à Brand Loyalty 0.246 0.115 2.134 0.033 H4 (supported)

Brand satisfaction à WOM 0.429 0.085 5.076 *** H5 (supported)

Brand Loyalty à WOM 0.395 0.139 2.843 0.004 H6 (supported)

Note: *** = sign < 0.01

The results of the multi-group model analysis, showing the moderating effect of the length of time as a customer on the relationships between the variables, are shown in Table 7. There was a significant difference between the established users and new users in the relationship between brand experience and satisfaction. The coefficient for brand experience on brand satisfaction was higher for established users (β = 1.198, CR = 10.342) than for new users (β = 0.865, CR =10.985);

the value of the critical ratio for differences between parameters of these two groups was 2.377.

Thus, H7a was supported. The moderating effect of the duration of being a customer on the effect of brand experience on brand loyalty was positive and significant. The coefficient of brand

(13)

experience on brand loyalty was lower for established users (β = 0.685, CR = 5.107) than for new users (β = 0.795, CR = 5.198), while the value of the critical ratios for differences between parameters of these two groups was -0.542. Thus, H7b was rejected. There was a significant negative difference between established users and new users concerning the effect of brand experience on WOM recommendations. The coefficient of brand experience on brand loyalty was lower for established users (β = 0.073, CR = 0.339) than for new users (β = 0.790, CR = 2.977), while the value of the critical ratio for differences between parameters of these two groups was - 2.101. Thus, H7c was supported. The moderating effect of the duration of being a customer on the effect of brand satisfaction on brand loyalty was positive and significant. The coefficient of brand satisfaction on brand loyalty was higher for established users (β = 0.517, CR = 4.578) than for new users (β = 0.110, CR = 0.648), while the value of the critical ratio for differences between parameters of these two groups was 1.996. Thus, H8a was supported. The moderating effect of the duration of being a customer on the effect of brand satisfaction on WOM recommendations was negative and significant. The coefficient of brand satisfaction on WOM recommendations was lower for established users (β = 0.114, CR = 0.687) than for new users (β = 0.590, CR = 3.422), while the value of the critical ratio for differences between parameters of these two groups was - 1.991. Thus, H8b was supported. The moderating effect of the duration of being a customer on the effect of brand loyalty on WOM recommendations was expected to be negative, but the result was positive and significant. The coefficient of brand satisfaction on brand loyalty was higher for established users (β = 0.690, CR = 2.419) than for new users (β = -0.067, CR = -0.322), while the value of the critical ratio for differences between parameters of these two groups was 2.144.

Thus, H9 is rejected.

Table 7. Summary of the moderating effect of the status of using a product.

Proposed hypothesis

Old user New user Critical Ratio for Differences

between Parameters

Rejected/

supported Estimate CR Estimate CR

brand experienceà

satisfaction 1.198 10.342 0.865 10.985 2.377 H7a

(supported) brand experience à

loyalty 0.685 5.107 0.795 5.176 -0.542 H7b

(rejected) brand experience à

WOM 0.073 0.339 0.790 2.977 -2.101 H7c

(supported) brand satisfaction à

loyalty 0.517 4.578 0.110 0.648 1.996 H8a

(supported) brand satisfaction à

WOM 0.114 0.687 0.590 3.422 -1.991 H8b

(supported)

brand loyalty à WOM 0.690 2.419 -0.067 -0.322 2.144 H9

(rejected) Further details of the effect of the status of using a product on the relationship between brand experience, satisfaction, loyalty, and WOM recommendations are shown in Figure 3. Figures 3a, 3d and 3f show that the slope of the regression line for the established user group was greater than for the new user group. Conversely, Figure 3b, 3c and 3e show that the slope of the regression line for the established user group was lower than that for the new user group. Thus,

(14)

the moderating effect of the status of using a product was positive for the relationships experience-satisfaction, satisfaction-loyalty, loyalty-WOM. In contrast, the status of using a product had a negative moderating effect on the relationships satisfaction-WOM and experience- WOM. The status of using a product had no significant moderating effect on the relationship between brand experience and loyalty.

DISCUSSION

The purpose of this study was to investigate the relationship between brand experience, satisfaction, loyalty, and WOM recommendations by examining the effects of the moderating variable: the status of using the product. Hypothesis testing showed that brand experience directly influenced customer satisfaction, brand loyalty and WOM recommendations. This result was consistent with the findings of several studies (Klaus & Maklan, 2013; Chen et al., 2014; Khan

& Rahman, 2015) which found a positive effect for the quality of experience on brand satisfaction.

Brand experience also had a positive effect on brand loyalty, which was consistent with previous studies (Khan & Rahman, 2015; Moreira et al., 2017; Schmitt, 2003). In addition, the results of this study were also consistent with the previous studies (Beckman et al., 2013; Barnes et al., 2014; Chen et al., 2014; Klaus & Maklan, 2013; Oliver, 1997), which showed that brand experience had a positive effect on WOM recommendations. The positive effect of brand experience on satisfaction, brand loyalty and WOM recommendations implied that the more customers have a positive experience, the higher their satisfaction, brand loyalty and the more positive their WOM recommendations. These results suggested that, in the context of the women's apparel industry, the visual, sensory, and emotional experience of customers is important in increasing customer satisfaction, customer loyalty and positive WOM recommendations to family and friends about using the product.

We found that brand satisfaction had a significant positive effect on brand loyalty and WOM recommendations, which was consistent with the findings of previous studies (Klaus & Maklan, 2013; Moreira et al., 2017) that loyalty leads to positive behavioural intentions and customer behaviour and our results also found that this. Our results showed that the higher the brand satisfaction, the higher the customer loyalty, which in turn encouraged consumers to talk about the brand positively. Thus, it was concluded that brand experience could affect WOM recommendations both directly and indirectly (through brand satisfaction and loyalty).

(15)

We found support for moderation effects by variable the status of using the product on brand experience-satisfaction, brand satisfaction-loyalty, and brand loyalty-WOM. These results indicated that the brand experience-satisfaction, satisfaction-loyalty, and loyalty-WOM relationships were stronger in the established user group. This result was consistent with previous research (Alba & Hutchinson, 1987; Rao & Monroe, 1988; Walsh et al., 2008); which confirmed that the higher the consumer's knowledge, the higher the consumer's trust and objectivity in assessing a brand, and that, in turn, can increase satisfaction and loyalty. We also found a negative moderating effect of variable the status of using the product on the relationships of brand experience-WOM and brand satisfaction-WOM. We also found that the relationships brand experience-WOM and brand satisfaction-WOM were stronger in the new user group. These results were consistent with the "honeymoon effect" and previous research (Ranaweera and Menon 2013) which showed that the effect of brand experience and brand satisfaction on WOM recommendations was stronger in newer groups. In contrast, our study also found that the effect of brand loyalty on WOM recommendations was stronger in the established user group, a result that was contrary to the "honeymoon effect". To explain this unexpected moderation effect, we suggest that established customers may have more information to tell friends, family and others.

The key implication of our study is that managers should try to improve the positive experience of customers without paying attention to their relationship with WOM integrative. Increasing brand experience can increase WOM recommendations (especially positive WOM recommendations) both directly and indirectly through brand satisfaction and brand loyalty.

Furthermore, if the ultimate goal is to increase the role of WOM recommendations as a marketing function, it is better to focus on new users. Conversely, if the goal is to increase satisfaction and loyalty, it is better to focus on the established user group.

CONCLUSION

We concluded that brand experience affected brand satisfaction, and loyalty could be created, which in turn increased the WOM recommendations. This study investigated brand experience and its relationship to brand satisfaction, brand loyalty and WOM recommendations, and which was moderated by the status of using the product, something that had not been investigated before. The test results show that the relationship between these variables is proven to depend on the status of product use, except for the relationship between experience-loyalty and satisfaction-WOW. This research in the area of women's underwear products has created new avenues for investigating these concepts in a rapidly developing area of research. From a practical point of view, our research provides evidence for the usefulness of brand experience as a means of increasing WOM recommendations. Companies need to formulate a marketing strategy by developing or creating products that can provide positive experiences for customers.

LIMITATIONS AND FUTURE RESEARCH

This study has several limitations. The study sample only includes product customers with the Wacoal brand with offline service experience. Therefore, care must be taken when generalizing the results to potential customers without brand experience. The number of samples taken is relatively small, although this number is in accordance with the recommendations in the literature (Yoon and Suh 2003), it will be useful to increase the number of customer samples to increase the accuracy of the results. Various future research paths emerge from this research.

First, it will be useful to analyze this causal model, not only the status of product use, but a variety

(16)

of other moderating variables can be developed (such as: level of product use, product knowledge, buying habits etc.). For the WOM variable, it can be separated between positive WOM versus negative WOM and traditional WOM versus electronic WOM (Ranaweera and Menon 2013). We assume that behavior will not be the same among these variables, which can have a major impact on marketing strategy. Finally, future research might also be tested in other consumer products to verify whether the results are generalizable.

Reference

Aghekyan, Marine, Sandra Forsythe, Wi-Suk Kwon, and Veena Chattaraman. 2012. “The Role of Product Brand Image and Online Store Image on Perceived Risks and Online Purchase Intentions for Apparel.” Journal of Retailing and Consumer Services 19 (May): 325–331.

Alba, Joseph W, and J Wesley Hutchinson. 1987. “Dimensions of Consumer Expertise.” Journal of Consumer Research 13 (4): 411–54.

Alexandrov, Aliosha, Bryan Lilly, and Emin Babakus. 2013. “The Effects of Social- and Self-Motives on the Intentions to Share Positive and Negative Word of Mouth.” Journal of the Academy of Marketing Science 41 (5): 531–46.

Algesheimer, René, Utpal M. Dholakia, and Andreas Herrmann. 2005. “The Social Influence of Brand Community:

Evidence from European Car Clubs.” Journal of Marketing 69 (3): 19–34.

Arnold, HJ. 1992. “Moderator Variables: A Clarification of Conceptual, Analytic, and Psychometric Issues.” Organ Behav Hum Perform 34: 214–24.

Babin, Barry J., David M. Hardesty, and Tracy A. Suter. 2003. “Color and Shopping Intentions: The Intervening Effect of Price Fairness and Perceived Affect.” Journal of Business Research 56 (7): 541–51.

Bagozzi, Richard P., and Youjae Yi. 1988. “On the Evaluation of Structural Equation Models.” Journal of the Academy of Marketing Science 16 (1): 74–94.

Barnes, Stuart J., Jan Mattsson, and Flemming Sørensen. 2014. “Destination Brand Experience and Visitor Behavior:

Testing a Scale in the Tourism Context.” Annals of Tourism Research 48: 121–39.

Baron, Reuben M., and David A. Kenny. 1986. “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations.” Journal of Pesonality and Social Psychology 51 (6):

1173–82.

Beckman, Eric, Archana Kumar, and Youn Kyung Kim. 2013. “The Impact of Brand Experience on Downtown Success.” Journal of Travel Research 52 (5): 646–58.

Berry, Leonard L., Kathleen Seiders, and Dhruv Grewal. 2002. “Understanding Service Convenience.” Journal of Marketing 66 (3): 1–17.

Bianchi, Constanza, Judy Drennan, and Bill Proud. 2014. “Antecedents of Consumer Brand Loyalty in the Australian Wine Industry.” Journal of Wine Research 25 (2): 91–104.

Blut, Markus, Christof Backhaus, Tobias Heussler, David M. Woisetschläger, Heiner Evanschitzky, and Dieter Ahlert.

2011. “What to Expect After the Honeymoon: Testing a Lifecycle Theory of Franchise Relationships.” Journal of Retailing 87 (3): 306–19.

Bougoure, Ursula Sigrid, Rebekah Russell-Bennett, Syed Fazal-E-Hasan, and Gary Mortimer. 2016. “The Impact of Service Failure on Brand Credibility.” Journal of Retailing and Consumer Services 31: 62–71.

Brakus, J Josko, Bernd H Schmitt, and Lia Zarantonello. 2009. “Brand Experience: What Is It? How Is It Measures?

Does It Affect Loyalty?” Journal of Marketing 73 (May): 52–68.

Bryman, A., and D. Cramer. 1999. Quantitative Data Analysis with SPSS Release 8 for Windows: A Guide for Social Scientists. Routledge, London.

Carroll, Barbara A., and Aaron C. Ahuvia. 2006. “Some Antecedents and Outcomes of Brand Love.” Marketing Letters 17 (2): 79–89.

(17)

Archives of Business Research (ABR) Vol 9, Issue 2, February-2021 Chen, Hsin., Anastasia Papazafeiropoulou, Ta-kang Chen, Yanqing Duan, and Hsiu-wen Liu. 2014. “Exploring the Commercial Value of Social Networks Enhancing Consumers’ Brand Experience through Facebook Pages.” Journal of Enterprise Information Management 27 (5): 576–598.

Chung, Cindy M.Y., and Qianyi Tsai. 2009. “The Effects of Regulatory Focus and Tie Strength on Word-of-Mouth Behaviour.” Asia Pacific Journal of Marketing and Logistics 21 (3): 329–41.

Clemes, Michael D., Christopher Gan, and Min Ren. 2011. “Synthesizing the Effects of Service Quality, Value, and Customer Satisfaction on Behavioral Intentions in the Motel Industry: An Empirical Analysis.” Journal of Hospitality and Tourism Research 35 (4): 530–68.

Cohen, Jacob. 1988. Statistical Power Analysis for the Behavioral Sciences. 2nd ed. NJ: Erlbaum: Mahwah.

Daugherty, Terry, and Ernest Hoffman. 2014. “EWOM and the Importance of Capturing Consumer Attention within Social Media AU - Daugherty, Terry.” Journal of Marketing Communications 20 (1–2): 82–102.

Derbaix, Christian, and Joëlle Vanhamme. 2003. “Inducing Word-of-Mouth by Eliciting Suprise - A Pilot Investigation.”

Journal of Economic Psychology 24 (1): 99–116.

Edward, Manoj, and Sunil Sahadev. 2011. “Role of Switching Costs in the Service Quality, Perceived Value, Customer Satisfaction and Customer Retention Linkage.” Asia Pacific Journal of Marketing and Logistics 23 (June): 327–45.

Fitzgerald Bone, Paula. 1992. “Determinants of Word-Of-Mouth Communications During Product Consumption.”

Advances in Consumer Research 19 (1): 579–83.

Fornell, Claes. 1992. A National Customer Satisfaction Barometer: The Swedish Experience. Journal of Marketing. Vol.

56.

Fornell, Claes, and F David Larker. 1981. Evaluating Structural Equation Models With Unobservable Variable Sand Measurement Error. Journal of Marketing Research. Vol. 18.

Fullerton, Gordon. 2005. The Impact of Brand Commitment on Loyalty to Retail Service Brands. Canadian Journal of Administrative Sciences / Revue Canadienne Des Sciences de l’Administration. Vol. 22.

Gremler, Dwayne D., and Stephen W. Brown. 1999. “The Loyalty Ripple Effect.” International Journal of Service Industry Management 10 (3): 271–93.

Hair, Joseph F., William C. Black, Barry J. Babin, and Ralph E. Anderson. 2014. Multivariate Data Analysis. Pearson Custom Library. https://doi.org/10.1038/259433b0.

Harrison-Walker, L. Jean. 2001. “The Measurement of Word-of-Mouth Communication and an Investigation of Service Quality and Customer Commitment As Potential Antecedents.” Journal of Service Research 4 (1): 60–75.

Hellier, Phillip K., Gus M. Geursen, Rodney A. Carr, and John A. Rickard. 2003. “Customer Repurchase Intention.”

European Journal of Marketing 37 (11/12): 1762–1800.

Ismail, Ahmed Rageh, and Gabriella Spinelli. 2012. “Effects of Brand Love, Personality and Image on Word of Mouth:

The Case of Fashion Brands among Young Consumers.” Journal of Fashion Marketing and Management: An International Journal 16 (4): 386–98.

Jeong, Hyo Jin, and Dong Mo Koo. 2015. Combined Effects of Valence and Attributes of E-WOM on Consumer Judgement for Message and Product The Moderating Effect of Brand Community Type. Internet Research. Vol. 25.

Kazemi, Ali, Vahid Moradi PaEmami, Arash Abbaszadeh, and Javad Pourzamani. 2013. “Impact of Brand Identity on Customer Loyalty and Word of Mouth Communications, Considering Mediating Role of Customer Satisfaction and Brand Commitment. (Case Study: Customers of Mellat Bank in Kermanshah).” International Journal of Academic Research in Economics and Management Sciences 2 (4): 1–14.

Khan, Imran, and Zillur Rahman. 2015. “A Review and Future Directions of Brand Experience Research.”

International Strategic Management Review 3 (1–2): 1–14.

Klaus, Philipp, and Stan Maklan. 2013. “Towards a Better Measure of Customer Experience.” International Journal of Market Research 55 (2): 227–46.

(18)

Lai, Ivan Ka Wai. 2014. “The Role of Service Quality, Perceived Value, and Relationship Quality in Enhancing Customer Loyalty in the Travel Agency Sector.” Journal of Travel & Tourism Marketing 31 (3): 417–42.

López, Manuela, and María Sicilia. 2013. “How WOM Marketing Contributes to New Product Adoption: Testing Competitive Communication Strategies.” European Journal of Marketing 47 (7): 1089–1114.

Matos, Celso De, and Carlos Rossi. 2008. “Word-of-Mouth Communications in Marketing: A Meta-Analytic Review of the Antecedents and Moderators.” Journal of the Academy of Marketing Science 36 (December): 578–96.

Merton, Robert K. 1968. Social Theory and Social Structure. Enlarged e. New York: The Free Press.

Mishra, Abhishek B., Satya Bhushan Dash, and Dianne Cyr. 2014. “Linking User Experience and Consumer-Based Brand Equity: The Moderating Role of Consumer Expertise and Lifestyle.” Journal of Product & Brand Management 23 (4/5): 333–48.

Mohsan, Faizan, Muhammad Musarrat Nawaz, M. Sarfraz Khan, Zeeshan Shaukat, and Numan Aslam. 2011. “Impact of Customer Satisfaction on Customer Loyalty and Intentions to Switch : Evidence from Banking Sector of Pakistan.”

International Journal of Business and Social Science 2 (16): 263–70.

Moreira, Carriso Antonio, Pedro Miguel Freitas, and Víctor Manuel Ferreira. 2017. “The Effects of Brand Experiences on Quality, Satisfaction and Loyalty: An Empirical Study in the Telecommunications Multiple-Play Service Market.”

Revista Innovar Journal Revista de Ciencias Administrativas y Sociales 27 (64): 23–36.

Morrison, Sharon, and G. Frederick Crane. 2007. “Building the Service Brand by Creating and Managing an Emotional Brand Experience.” Journal of Brand Management (2007) 14 (5): 410 – 421.

Nysveen, Herbjørn, and Per Egil Pedersen. 2014. “Influences of Co-Creation on Brand Experience: The Role of Brand Engagement.” International Journal of Market Research 56 (6): 807–32.

Oliver, Riscrd L. 1997. Satisfaction A Behavioral Perspective On The Consumer. Singapore: McGraw-Hill Education.

Ramaseshan, B, and Alisha Stein. 2014. “Connecting the Dots between Brand Experience and Brand Loyalty: The Mediating Role of Brand Personality and Brand Relationships.” The Journal of Brand Management 21 (November).

https://doi.org/10.1057/bm.2014.23.

Ranaweera, Chatura, and Kalyani Menon. 2013. “For Better or for Worse? Adverse Effects of Relationship Age and Continuance Commitment on Positive and Negative Word of Mouth.” European Journal of Marketing 47 (September):

1598–1621.

Rao, Akshay R, and Kent B Monroe. 1988. “The Moderating Effect of Prior Knowledge on Cue Utilization in Product Evaluations*.” Journal of Consumer Research 15 (2): 253–64.

Schmitt, Bernd. H. 2003. Customer Experience Management. John Wiley & Sons, Inc.

Sekaran, Uma, and Roger Bougie. 2016. Research Methods for Business.

Shi, Wenhua, Lingshu Tang, Xiaohang Zhang, Yu Gao, and Yameng Zhu. 2016. “How Does Word of Mouth Affect Customer Satisfaction?” Journal of Business and Industrial Marketing 31 (3): 393–403.

Sicilia, Maria, Elena Delgado-Ballester, and Mariola Palazon. 2016. “The Need to Belong and Self-Disclosure in Positive Word-of-Mouth Behaviours: The Moderating Effect of Self–Brand Connection.” Journal of Consumer Behaviour 15 (1): 60–71.

Sweeney, Jill, Geoff Soutar, and Tim Mazzarol. 2014. “Factors Enhancing Word-of-Mouth Influence: Positive and Negative Service-Related Messages.” European Journal of Marketing 48 (1): 336–59.

Sweeney, Jillian, Geoffrey N. Soutar, and Tim Mazzarol. 2008. “Factors Influencing Word of Mouth Effectiveness:

Receiver Perspectives.” European Journal of Marketing 42 (3–4): 344–64.

Tsai, Ming Tien, Chung Lin Tsai, and Han Chao Chang. 2010. “The Effect of Customer Value, Customer Satisfaction, and Switching Costs on Customer Loyalty: An Empirical Study of Hypermarkets in Taiwan.” Social Behavior and Personality 38 (6): 729–40.

Tuu, Ho Huy, Svein Ottar Olsen, and Pham Thi Thuy Linh. 2011. “The Moderator Effects of Perceived Risk, Objective Knowledge and Certainty in the Satisfaction-Loyalty Relationship.” Journal of Consumer Marketing 28 (5): 363–75.

(19)

Archives of Business Research (ABR) Vol 9, Issue 2, February-2021 Virvilaite, Regina, Dovile Tumasonyte, and Laimona Sliburyte. 2015. “The Influence of Word of Mouth

Communication on Brand Equity: Receiver Perspectives.” Procedia - Social and Behavioral Sciences 213: 641–46.

Voyer, Peter A., and Chatura Ranaweera. 2015. “The Impact of Word of Mouth on Service Purchase Decisions:

Examining Risk and the Interaction of Tie Strength and Involvement.” Journal of Service Theory and Practice 25 (5):

636–56.

Walsh, Gianfranco, Heiner Evanschitzky, and Maren Wunderlich. 2008. “Identification and Analysis of Moderator Variables: Investigating the Customer Satisfaction-Loyalty Link.” European Journal of Marketing 42 (9–10): 977–

1004.

Westbrook, Robert A. 1987. “Product/Consumption-Based Affective Responses and Postpurchase Processes.” Journal of Marketing Research 24 (3): 258–70.

Wien, Anders Hauge, and Svein Ottar Olsen. 2012. “Evaluation Context’s Role in Driving Positive Word-of-mouth Intentions.” Journal of Consumer Behaviour Volume11 (Issue6): 504–513.

Yi, Youjae, and Suna La. 2004. “What Influences the Relationship between Customer Satisfaction and Repurchase Intention? Investigating the Effects of Adjusted Expectations and Customer Loyalty.” Psychology and Marketing 21 (5): 351–73.

Yoon, Mahn, and Jaebeom Suh. 2003. “Organizational Citizenship Behaviors and Service Quality as External Effectiveness of Contact Employees.” Journal of Business Research 56 (August): 597–611.

Zhang, Xiaoquan Michael, and Chrysanthos Chris Dellarocas. 2006. “The Lord of the Ratings: How a Movie’s Fate Is Influenced by Reviews?” ICIS 2006 Proceedings - Twenty Seventh International Conference on Information Systems, 1959–78.